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A Rational Workflow for Sequential Virtual Screening of Chemical Libraries on Searching for New Tyrosinase Inhibitors Huong Le-Thi-Thu1,*, Gerardo M. Casañola-Martín2,3,4, Yovani Marrero-Ponce5, Antonio Rescigno6, Concepción Abad2 and Mahmud Tareq Hassan Khan7 1

School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam; 2Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100 Burjassot, Spain; 3Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain; 4Centro de Información y Gestión Tecnológica, Ministerio de Ciencia Tecnología y Medio Ambiente (CITMA), 65100, Ciego de Avila, Cuba; 5Enviromental and Computational Chemistry Group, Facultad de Química Farmacéutica, Universidad de Cartagena, Cartagena de Indias, Bolivar, Colombia; 6Sezione di Chimica Biologica, Dip. Scienze e Tecnologie Biomediche, Università di Cagliari, Cittadella Universitaria, 09042 Monserrato (CA), Italy; 7Present address: Holmboevegen 3B, 9010 Tromso, Norway Abstract: The tyrosinase is a bifunctional, copper-containing enzyme widely distributed in the phylogenetic tree. This enzyme is involved in the production of melanin and some other pigments in humans, animals and plants, including skin pigmentations in mammals, and browning process in plants and vegetables. Therefore, enzyme inhibitors has been under the attention of the scientist community, due to its broad applications in food, cosmetic, agricultural and medicinal fields, to avoid the undesirable effects of abnormal melanin overproduction. However, the research of novel chemical with antityrosinase activity demands the use of more efficient tools to speed up the tyrosinase inhibitors discovery process. This chapter is focused in the different components of a predictive modeling workflow for the identification and prioritization of potential new compounds with activity against the tyrosinase enzyme. In this case, two structure chemical libraries Spectrum Collection and Drugbank are used in this attempt to combine different virtual screening data mining techniques, in a sequential manner helping to avoid the usually expensive and time consuming traditional methods. Some of the sequential steps summarize here comprise the use of drug-likeness filters, similarity searching, classification and potency QSAR multiclassifier systems, modeling molecular interactions systems, and similarity/diversity analysis. Finally, the methodologies showed here provide a rational workflow for virtual screening hit analysis and selection as a promissory drug discovery strategy for use in target identification phase.

Keywords: Drug-likeness filtering, molecular docking, QSAR modeling, similarity searching, tyrosinase inhibitor, virtual screening. 1. INTRODUCTION Tyrosinase (monophenol monooxygenasse; EC 1.1.4.18.1) is a metalloenzyme oxidase widely distributed in the phylogenetic tree. This enzyme catalyze the two first steps of melanin synthesis pathway, by the hydroxylation of the L-tyrosine to 3,4-dihydroxyphenylalanine L-DOPA (monophenolase activity), and the posterior oxidation to dopaquinone (diphenolase activity) [1]. Because its main role in melanogenesis, abnormal tyrosinase regulation it is related with some skin diseases such as hyperpigmentation, melasma (acquired hyperpigmentation), post in ammatory melanoderma, solar lentigo, etc [2-4]. Hence, tyrosinase inhibitors have been used largely as depigmenting agents for treatment of these pigmentation disorders [5-7]. *Address correspondence to this author at the School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam; Tels: 53-33-223066 (Cuba) and 963543156 (València); Faxes: 53-33-223066 (Cuba) and 963543156 (València); E-mails: [email protected] or [email protected] 1873-5294/14 $58.00+.00

In recent times, besides QSAR methodologies, there are other data mining techniques introduced in drug discovery with high accuracy levels [8]. This successful data integration is a complex theme in the desktop today´s researchers. In this sense the current drug discovery scenarios are introducing standard workflow for screening structural chemical libraries [9]. This issue presents advantages because compounds could be obtained by the direct purchase from the owners avoiding the time-consuming of synthesis or isolation process [10]. Moreover, in the last times, the campaigns associated with massive virtual HTS are continuously increasing are gaining on accuracy in the prioritization process of chemicals, due to the introduction of several ligand and structure based methodologies [11-12]. Therefore, to manage larger compounds libraries and cover the expectations of the drug discovery process, in silico virtual screening and computer-aided drug design have become increasingly important [13]. In our research group, this approach has been applied in the discovery of novel tyrosinase inhibitors (TIs) as cycloartane [14-15], diterpenoidal alkaloids [16], tetraketones [17], coumarin [18], and so © 2014 Bentham Science Publishers

1474 Current Topics in Medicinal Chemistry, 2014, Vol. 14, No. 12

on. In these studies, the classification QSAR-based virtual screening (VS) has been employed for in-house congeneric chemical libraries of different laboratories to identify TIs from inactives. Latter, new class of QSAR models named potency models [19-20] were developed. The last models could be used as a cascade system together with the classification models for more complete description of tyrosinase inhibitory activity. Besides, this type of models helps to identify true positives and make an adequate process of prioritization of compounds. In recent work, all these models were assembled in different multi-classifier systems (MCSs) that improved the performance of QSAR methods [21]. By this means, in this chapter we present a procedure of combining these and others different VS strategies in the computational research for the selection/identification of novel tyrosinase inhibitors. This framework was employed with efficacy to discover new chemical entities with antityrosinase activity. Finally, is important to stand out that the different virtual screening approaches mentioned comprises: drug-likeness filters, similarity searching, classification and potency QSAR multi-classifier systems, molecular docking studies, and post-processing procedures as strategies; that were assessed in a sequential manner over the Spectrum Collection and Drugbank databases. 2. TWO STRUCTURE CHEMICAL LIBRARIES: SPECTRUM COLLECTION AND DRUGBANK The ascending grown of computational resources have brought a rapid increasing of structural chemical databases either online or repository company. The more interesting examples are the huge ZINC and ChemSpider databases, comprising 13 million and 26 million of compounds, respectively. This two chemical data sources are included between the main sixty-four free databases nowadays [22]. Therefore, the huge structural chemical database screening is becoming one of the hotter topics in compound retrieve using any QSAR, ligand or structure data mining procedures. In this sense, some authors have included interesting updated reviews about this topic [23-24]. By this mean here we presented the results obtained over the Spectrum collection (http://www.msdiscovery.com/spectrum.html) and Drugbank (www.drugbank.ca), which consists of 2 000 and 6 827

Le-Thi-Thu et al.

compounds, respectively, that were screened using a sequential strategy for virtual screening looking for potential therapeutic chemicals for the treatment of hyper-pigmentation disorders. A owchart depicting the various steps of virtual screening including database ltration, similarity searching, QSAR modeling, docking and clustering studies to prioritize the virtual hits is shown in (Fig. 1). This Fig. (1) displays the virtual screening stepwise workflow which resulted in the discovery of novel scaffolds against the tyrosinase enzyme. The protocol was based in the computational hierarchy of each filter, the consuming CPU time and the complexity of input information for each step. This hierarchical procedure allows reducing the number of selected compounds (retrieved as novel TIs) gradually after each filter. This mentioned strategy was employed to screen virtually two databases (Spectrum Collection and Drugbank). By other way, many of the chemical libraries as the case of PubChem [25] on-line database are web-based systems with well recognize facilities to do some pre-processing tasks in an easy way. This is the also the case Spectrum collection and Drugbank were some drug-likeness filters or similarity searching methods are implemented as tools for search and retrieving. Therefore some of these services were used in these studies. 3. DRUG-LIKENESS FILTERS The term “druglike” [26-29] is used for pharmaceutical research to describe molecules with properties that fall within the boundaries delineated by the wide majority of pharmaceutical agents. This process is associated with the many possible molecular properties that most directly influence the drug-like properties of a molecule in some specific type of research. Lipinski et al.[30] defined the so-called “rule of five” (sometimes abbreviated as RoF) in an effort to solve this question. The main steps of this concept is the examination of different parameters such as the number of rotatable bonds (nRotB), polar surface area (PSA), log D, and counts of nitrogen and oxygen atoms in an effort to define easily calculated properties that will be predictive of a favorable outcome and established mayor cutoff for these physi-

Fig. (1). Sequential virtual screening workflow used in the identification of promissory TIs and the filtering of compounds involved in each one of different steps from the Spectrum Collection and DrugBank databases.

Virtual Screening Workflow for the Identification of New Tyrosinase Inhibitors

cal-chemical properties and others [31-35]. However the threshold of Lipinski seems very rigid in occasions. Hence some scientist in this field have stand out and proposed other diverse boundaries and criteria of drug-likeness filters [36]. Taking this into consideration, in our work we applied superior limits of all these filters. In our case, a compound was not taken into consideration in the next steps if it has the molecular weight (MW) above 700 g/mol; the computed octanol–water partition coefficient CLogP higher than 7; the number of hydrogen bond donors (nHBDon) above 5 and acceptors (nHBAc) above 10; the number of rotatable bonds (nRotB) higher than 10 and a polar surface area (PSA) above 140 Å2 . All these descriptors were calculated with our in house TOMOCOMD-CARDD (acronym for TOpological MOlecular COMputational Design - Computed-Aided ‘Rational’ Drug Design) software. These molecular descriptors are implemented in a new module (DESPOOLs, acronym of DEScriptor POOLs) of our program [37] that offers calculations of the several 0-3D indices, which are calculated mainly using The Chemistry Development Kit [38]. By using the defined way above we proceed to the first filtering consisting in the application of the criteria describe above on the Spectrum collection database (http://www.msdiscovery.com/spectrum.html). This first step also consists of reducing the number of chemicals (negative design) employing the Drug-likeness filters. These are simple, fast and also allow “optimizing” in some way simultaneously the potency and the pharmacokinetic [39]. So, we further sorted these 2 000 compounds using the superior boundaries of all filters reported in the literature and nally 1 394 compounds were further considered for the next step. 4. SIMILARITY SEARCHING Similarity searching identifies those database molecules that are most similar to reference structures, using some quantitative definition of intermolecular structural similarity. The reference structures and the molecules in the database are characterized by one or more molecular descriptors. Their comparisons allow the calculation of a similarity measurement between the reference structure and each of the database structures, and the latter ones are then sorted into order of decreasing similarity with the target. The output from the search is a ranked list in which the structures that are calculated to be most similar to the reference structure are located at the top of the list. These chemicals will be those that have the greatest probability of being of interest to the user, given an appropriate measure of intermolecular structural similarity. The similarity methods are extremely useful at the beginning of a drug discovery project, because it needs little information about the target and only few known active compounds. Moreover, the implementations of similarity methods are generally computationally inexpensive, so searching large databases can be routinely performed. The result of this step is a focused library, since all included compounds present common features a reference compounds. In our case the data fusion method [40] was applied. . In Table 1, the structures of reference compounds were given. A hierarchical cluster analysis, k-NNCA, was executed to visualize the distribution of reference compounds in different groups. In Fig. (2), a dendogram for

Current Topics in Medicinal Chemistry, 2014, Vol. 14, No. 12

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these compounds is shown. It can be seen, there is great structural diversity among these chemicals, which represent different molecular subsystems important for the tyrosinase activity. The set of 15 strong tyrosinase inhibitors of diverse structures was selected as reference compounds. The molecular structures of these chemicals are given in Table 1. The MACCS fingerprints [41] were calculated to characterize reference structures and the ones of the database employing the program TOMOCOMD-CARDD software. These fingerprint are implemented in a new module (MOLFIP, acronym of MOLecular FIngerPrints) of our program [37] that offers calculations of the several fingerprints, which are calculated mainly using The Chemistry Development Kit [38]. The Tanimoto coefficient [42], commonly used for binary data, was computed to establish the metrics of intermolecular comparison (each compound with every other in its activity class). A specific database molecule appears at rank position rij (1 i n, 1 j 15) with a similarity measurement (scores), sj against every reference structure. We used the fusion rule MAX for combining the similarity scores, so the final fused score was established as shown in Equation 1. sf = Maximum {s1, s2…s15} (1) Later, each molecule of the database was sorted by its fused score, sf. The similarly active compounds in the top 30% of highest ranked data set compounds were retrieved for the next step. For the case of the Spectrum collection database structures this procedure was applied resulting in the elimination of 1285 molecules. The remaining 109 compounds are similar in some way to one of 15 reference compounds (positive design). The Drugbank database (www.drugbank.ca) of 6 827 drugs was also screened using a similar procedure as above. In this case, first the similarity searching (data fusion by maximum score using 15 strong TIs as reference structures) was applied, because DrugBank offers this option in the management of its search database. By this procedure were eliminated 6659 compounds representing the 97.54% of the chemicals in the database. The repeated or reported against the tyrosinase compounds were removed and the remaining ones were ltered using Druglikeness criteria mentioned in the section above. From this, 131 compounds were selected and considered for the next step. 5. MULTICLASSIFIERS GUIDED BY QSAR MODELS In recent times, Quantitative Structure-Activity Relationships (QSARs) are the most widely used approach in drug design and have been applied successfully in the discovery of novel tyrosinase inhibitors [18, 43-49]. Hence, this method could constitute the principal “switch” for sequential workflow aiding to new lead compounds identification. The binary QSARs for tyrosinase inhibitors are described in previous reports [18, 43-49], therefore a brief approximation will be discussed here. A first training set of 1072 compounds was collected with 526 chemicals classi ed as “active” (TIs) and 546 compounds as “inactive” (non-TIs). The molecular structures and properties were correlated with biological activity using TOMOCOMD-CARDD descriptors,


Table 1.

Le-Thi-Thu et al.

Structures of reference compounds in similarity study.

O

O

O HO

O

N

HO

O

NH 2

2 L-mimosine

1 Kojic acid

N O N OH

OH

OH

3 L-Tropolone

4 N-cyclopenthyl-Nnitrosohydroxyl-amine

O HO

HO

H2 N S

O

HO

O

OH

N

HN N

O OH

5 Methyl ester of gentisic acid

O

O

6 Kurarinone

7 Phenyl-thiourea

8 BP4

HO

O

HO

OH

O HN

HO

O

OH

S

HO

O H O O O

O

11 4-Prenyloxy-

O

9 8´-epi-cleomiscosin A

resveratrol

10

12 Alkyl-thiocarbamate E

HO

NH 2

OH

S NH

O

N

NH

O O

O

HO OH

13 Benzylbenzamide 15

O

OH

14 3-Hydroxy -4-methoxy benzaldehyde thiosemicarbazone

and different classification models were generated. These models enable the identification of TIs from inactive ones. In second place, the potency models were obtained using a learning set of 257 strong TIs and 141 moderate-to-weak compounds [19-20]. The last ones would be used hierarchically with the models adjusted on the first database, for more complete description of tyrosinase inhibitory activity. Afterward, we introduced other statistical techniques [quadratic discriminant analysis (QDA), binary logistic regression (BLR) and classification tree (CT) [20]] and many machine learning approaches [support vector machine (SVM), artificial neural network (ANN), Bayesian networks (BNs), k-nearest neighbors (KNN) [19]], which enhanced the performance of previous LDA-QSAR models in both database. Theses single classifiers can be used to make tyrosinase inhibitory activity depictions for new chemicals. However, many factors can affect the performance of those

O NH 2

15 TK21

classifiers. Selecting the best available classifier is an option, but because the distribution of new chemicals that the classifier may meet during operation may vary (slightly or significantly depending on the application), this approach does not provide the best solution in all cases. Furthermore, because many classifiers are generally tried before a single classifier is selected, this approach also discards valuable information by ignoring the performance of all the other classifiers [50]. By this aim, the combination of multiple classifiers has been proposed in the field of machine learning to improve the performance of the single classifier approaches [51-53]. These multiple classifier systems (MCS) are based on the combination of several classifiers such that their union achieves higher performance than the stand-alone classifiers. Hence, an ensemble of classifiers is a set of classifiers, whose individual classification decisions are combined in some way [54]. Many studies have demonstrated that



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Fig. (2). Dendrogram illustrating the results of the hierarchical k-NNCA of strong TIs used as reference compounds.

ensembles often outperform their base models (the component models of the ensemble) if the base models perform well on novel examples and tend to make errors on different examples [55]. In the case of tyrosinase inhibitor QSAR equations, to increase performance demands in modeling tyrosinase activity the individual models obtained were assembled in different multi-classifier systems (MCSs) to improve their performance classifiers for tyrosinase inhibitory activity prediction [21]. For the Spectrum Collection, the compounds found by similarity searching were screened by QSAR (another positive design approach) using classification MCS based on average probability (AP) [21] to identify new TIs. Thus, 65/109 compounds were identified as active against tyrosinase enzyme. It is important to highlight that most of inactive compounds identified by QSAR were the last ones of the list of 109 compounds. The same occurs in the case of the Drugbank database were 119/131 were identified as actives. This justifies the selection of the cutoff value of similarity searching. The compounds identified by classification QSAR were sequentially screened by potency QSAR using boosting ensemble based on support vector machine [21]. This potency MCS identified 25 and 107 compounds from Spectrum Collection and Drugbank, respectively as strong TIs. 6. MODELING MOLECULAR INTERACTIONS SYSTEMS The next step was to use the molecular docking, that consists of posing each ligand into the binding site of the target. This gives a predicted binding mode for each database com-

pound, together with a measure of the quality of the fit of the compound in the target binding site. This information is used to rank the compounds with a view to selecting and experimentally testing a small subset for biological activity [56]. The docking calculations of strong TIs identified by QSARs in the mentioned above studies were performed using the ICM™ docking module with the default setup as earlier mentioned [57-59]. 6.1. Preparations of the Inhibitors and Target Molecules The 2D structure of the compound (in mol file format) was converted to 3D and energy minimized at the 3D space of ICM environment. The atom types using local chemical environment, Merck Molecular Force Field (MMFF) [60-66] formal charges and 3D topology were assigned. The lowest energy conformers of the compounds were then docked into the 3D space of the active site of the three dimensional structure of Tyrosinase (PDB ID: 3NQ1). All the docking calculations were performed using the “interactive docking” menu at the ICM environment. After docking the stack of docking poses were checked visually. Multiple stack conformations were selected based on their docking energies, rmsd values (compared between the docked model and x-ray conformation) and similarities to closely related x-ray crystal structures from PDB. Then the best conformations for the compound were finally chosen, and then the binding energy was calculated using ICM script. For each of the individual docked complexes the free energies of binding (Gcal) between the protein and ligand was calculated using ICM script utilizing Equations 2 and 3[67]. Gcal. = GH + Gel. + Gs + C (2)


Gcal. = GH +Gcol. +Gdes-sol. + Gs + C

(3)

Here, GH is the hydrophobic or cavity term, which accounts for the variation of water/non-water interface area. Gel is the electrostatic term composed of coulombic (Gcol) interactions and desolvation (Gdes-sol) of partial charges transferred from an aqueous medium to a protein core environment. The Gs is the entropic term which results from the decrease in the conformational freedom of functional groups buried upon complexation; and finally the C is a constant accounts for the change of entropy of the system due to the decrease of free molecules concentration (cratic factor), and loss of rotational/translational degrees of freedom [67]. After preparation of docking process the strong TIs identified by QSARs were subsequently docked in the active site of the tyrosinase (PDB ID: 3NQ1) using the ICM program [57]. In this study, only the chemicals selected of the Spectrum Collection were used on the structure-based study. Docking is an effective method for prioritizing ligands with favorable interactions with the receptor and can also be seen as a positive design. For each case, the binding energy (BE) was achieved and used as score to binding mode prediction of the compounds. First, we calculated the BR for a set of strong, moderate-weak and inactive compounds with known activity against the tyrosinase. The docking molecular interaction process revealed that only one compound of the total of 25 selected by QSAR did not complement favorably the protein binding site. This result showed a good correspondence between QSAR and docking approaches. 7. POST-PROCESSING ANALYSIS Some methodologies for post-processing after sequential virtual screening were assessed. In our case, we selected the k-NNCA (k-nearest neighbors cluster analysis) and k-MCA (k-means cluster analysis) algorithms [68-69] to study the similarity/diversity among the retrieved active compounds and these latter ones with active compounds. This two types of Cluster Analysis (CA) were chosen because are a group of methods capable to recognize similarities among cases (objects) or among variables and single out some categories as a set of similar cases (or variables). Therefore it enables the selection of novel scaffold for tyrosinase inhibitors. Before carrying out the cluster processes, all the variables were standardized. In standardization, all values of selected variables (molecular descriptors) were replaced by standardized values, which are computed as follows: Std. score = (raw score - mean)/Std. deviation. Finally, by a CA of the database active compounds and the retrieved ones plus a detailed visual inspection, for the case of Spectrum Collection, 19 out of 24 compounds were selected to be evaluated experimentally. It is important to note that within the six compounds removed, some have been reported in the literature activity against tyrosinase, such as hinokitiol [70] and angolensine [71], while the Spectrum does not report itself. This fact confirmed the applicability of our protocol in the discovery of novel lead compounds anti-tyrosinase from large databases. Table 2 shows traditional uses and values of different "in silico" studies, the molecular structures of these compounds are given in Fig. (3).

Le-Thi-Thu et al.

On the case of Drugbank database, after cluster analysis and visual inspection we decided to select 32 compounds of the for enzyme assays. The structures of these compounds are shown in Table 3 and Table 4 shows traditional uses and values of different "in silico" studies for these drugs. The flowchart in Fig. (1) is a schematic representation of the rational workflow sequential VS process with the number of hits reduced for each screening step in both databases. Using the sequential workflow, a total of fifty one putative novel TIs were successfully identified, which can be purchased and further evaluated in enzymatic experimental corroborations. As it can be seen in both cases, many compounds identified as new TIs are already known drugs because and this avoids time-consuming to bring new drugs to market because re-discovered drugs that are already in use and its pharmacokinetic and toxicological properties are well-known [72]. This novel discovered drugs could be introduced into the market in the shortest time possible, thus accelerating the speed of discovery of new drugs for treating disorders of hyperpigmentation. 8. FUTURE TRENDS ON WORKFLOWS FOR BIOACTIVITY PREDICTION Bioactivity or any type of property prediction has always be one of the challenges on data mining fields. In the case of selection of adequate anti-target activity the main arduous task are mainly focused in the correct identification of lead compounds or promissory high activity chemicals that could lead to drug-like compounds after examining its ADMET properties. Many predictive workflows has been showed in literature most of them focused on the use of 3D-QSAR COMFA, COMSIA and pharmacophore approaches together with docking studies [73-74]. Because the drug discovery is a highly complex and costly process, the integration in innovation, knowledge, information, technologies, expertise, investments and management skills is required. In this way, the multistep VS can help identify bioactive substances from a large screening compound pool with limited experimental effort enabling to focus rapidly on the most promising candidate structures. In the case of the specific workflow scenarios, some questions that remains unsolved were derived during writing this chapter, like the use of sub-workflows integrated by several Multi-Sequential Workflow responsible of each step for adequate drug-like properties, that is ADMET. Moreover the consideration of other aspects concerning to workflow like the accuracy, sequence of combination, the most better quantity of sub-workflows to be used, and thresholds established for any workflow should be examined. Finally, these results offers a suitable alternative to the new era of open on-line chemical databases encouraging its use together with the ascending approaches based on the new technologies development such as massive computer calculations algorithms and cloud computing could have a overwhelming impact on virtual screening procedures based on ensemble workflows to solve several questions that are still in the route of drug discovery pipeline.


Table 2.


Results of different in silico filters of VS protocol on the Spectrum Collection database.

SimiID*

1500485

1479

46.17

0.825

7

Act

P

-3.7

6.00

5.00

95.34

0.816

10

Act

P

-2.2

1.00

2.00

4.00

55.76

0.786

19

Act

P

-3.1

4.89

1.00

4.00

tor, analgesic, antipyretic

468.21

2.35

0.00

-

208.07

1.75

Anticonvulsant,

sodium

antieleptic

BEk

2.00

251.08

Phenytoin

Docking

Sf g

LogPb

Bioactivity

Esti-

PSAf

MWa

Compound

Pre-

nHBDonc nHBAd nRotBe

dicted mated larity Ranking classh Potencyi

(Kcal/ mol)

Antinflamma1503801

1505130

Naproxol

3,4-Dimethoxycinnamic acid

200798

Dalbergione

-

270.09

2.36

1.00

3.00

4.00

63.60

0.767

31

Act

P

-5.2

1505311

Dibenzoylmethane

Antineoplastic

224.08

5.72

0.00

2.00

4.00

34.14

0.756

38

Act

P

-4.1

200090

Obtusaquinone

-

254.09

5.57

1.00

3.00

3.00

46.53

0.750

39

Act

P

-4.4

-

330.11

2.18

2.00

1.00

6.00

85.22

0.742

49

Act

P

-6.6

2',4-Dihydroxy3,4',6'-

1505140

trimethoxychalcone

1504152

Nilutamide

Antiandrogen

317.06

3.22

1.00

4.00

3.00

92.55

0.735

59

Act

P

-3.3

100308

Rockogenin

-

432.32

5.43

2.00

4.00

0.00

58.92

0.729

63

Act

P

-2.9

1503032

Dipyrocetyl

Antirheumatic, analgesic

238.05

1.59

1.00

4.00

5.00

89.90

0.724

67

Act

P

-0.7

300610

Acetosyringone

196.07

0.45

1.00

1.00

3.00

55.76

0.724

69

Act

P

-1.3

1504209

Diplosalsalate

analgesic, antipyretic

300.06

4.12

1.00

4.00

6.00

89.90

0.724

71

Act

P

-1.5

200034

Atranorine

-

374.10

3.48

3.00

4.00

6.00

130.36

0.719

77

Act

P

-5.9

1504118

Difractaic acid

-

374.14

3.37

2.00

3.00

6.00

102.29

0.719

79

Act

P

-6.5

1505186

Culmorin

-

238.19

3.70

2.00

2.00

0.00

40.46

0.714

82

Act

P

-2.4

201448

4,4'-Dimethoxy dalbergione

-

284.10

2.88

0.00

3.00

5.00

52.60

0.710

96

Act

P

-5.5

213.10

-0.21

3.00

5.00

4.00

86.63

0.708

98

Act

P

-1.9

Insect attractant, plant hormone

Glutamate receptor

2300228

Kainic acid

1505673

Troglitazone

Antidiabetic

441.16

3.52

2.00

3.00

5.00

110.16

0.700

111

Act

P

-7.2

330032

Dicamba

Herbicide

219.97

1.78

1.00

2.00

2.00

46.53

0.700

115

Act

P

-0.9

agonist, anthelmintic

*

ID =Code of Spectrum Collection;aMW = Molecular weight; bLogP = Computed octanol/water partition coefficient; cnHBDon = Number of hydrogen bond donors; dnHBAc = Num-

ber of hydrogen bond acceptors; fPSA = Polar surface area; gSf =Fused Score for the maximum of the similarity; hAct =Active against the tyrosinase identified by Clasiffication MCS QSAR; i P = Potent inhibitor of tyrosinase identified by Potency MCS QSAR; k BE =Binding energy (PDB ID: 3NQ1).


Le-Thi-Thu et al.

Fig. (3). Molecular structures of the identified virtual hits from Spectrum Collection by VS protocol. Table 3.

Molecular structures of virtual hits identified on Drugbank by current VS protocol. OH

OH

O

N

H O

O

HO H

O O

O H O

O O

DB00227

O

OH O

H

O

O

HO

O

DB00486

DB00769

DB00641 OH O

OH

HO H

HO

O

HO

O O

O O

O

O

O

O

HO

O

DB00929

O

DB02699

DB02329

DB02205 Br

OH

OH O

HN

H HO H H

OH

O

O

F

DB02880

O

OH

OH

HO

DB03451

DB03007

H H H

HO H

O

O H

O

DB03785 O

OH OH

O HO

O H

H

O O

DB04324

HO

H H

HO H

OH H O

H

DB04376

O

O

O O

O H

O

H O

N

O

O

DB04392

DB04599



1481

(Table 3) contd…. HO

F OH

HO

O

F

H

O H

H

HO

N

O H

HO

O

O

DB04641

H H

O

OH O

DB07036

OH

DB07177

DB07123

OH OH

H

H H

O

O

H

N

H H HO

O O

O

HO

OH

DB07500

H

N

O O

HN

H

H

DB07703

DB07567

SH

O

DB07734 O OH

H N

O

H H

O N O

SH

H

H NH 2

HO

O

H

H

H HO

DB07933

DB07883

O

OH

H

O

DB07735

H

DB08020

OH

OH H H

HO

OH H

H

O

H

O

O

O O

H HO

DB08224

H

H

N

O

H

Table 4.

HO H

O

H HO

HO

DB08442

DB08517

DB08737

Results of different in silico filters of virtual screening protocol on the DrugBank database.


PSAf

Similarity Predicted fused classh score

Estimated potencyi

ID*

Compound

Drug Group

MWa

LogPb

DB00227

Lovastatin

approved, investigational

404.26

4.574

1

5

7

72.83

0.56

Act

P

DB00486

Nabilone

approved, investigational

372.27

6.155

1

1

6

46.53

0.573

Act

P

DB00641

Simvastatin

approved

418.27

4.775

1

5

7

72.83

0.56

Act

P

DB00769

Hydrocortamate

approved

475.29

1.235

2

7

8

104.14

0.618

Act

P

DB00929

Misoprostol

approved

382.27

3.567

2

5

14

83.83

0.557

Act

P

DB02205

6-(1.1-Dimethylallyl)-2-(1Hydroxy-1-Methylethyl)2.3-Dihydro-7h-Furo[3.2G]Chromen-7-One

experimental

314.15

3.44

1

2

3

55.76

0.629

Act

P

DB02329

Carbenoxolone

experimental

570.36

6.855

2

7

6

117.97

0.759

Act

P

DB02699

4-Oxoretinol

experimental

300.21

4.185

1

2

5

37.3

0.553

Act

P


Le-Thi-Thu et al.

(Table 4) contd….

Drug Group

MWa

LogPb

DB02880

N-[1-(4-Bromophenyl)Ethyl]-5-Fluoro Salicylamide

experimental

337.01

2.77

2

2

4

49.33

0.551

Act

P

DB03007

9r.13r-Opda

experimental

292.20

4.903

1

3

11

54.37

0.551

Act

P

DB03451

1alpha.25-Dihydroxyl-20Epi-22-Oxa-24.26.27Trihomovitamin D3

experimental

460.355 3

5.452

3

4

9

69.92

0.623

Act

P

DB03785

(3r.5r)-7-((1r.2r.6s.8r.8as)2.6-Dimethyl-8-{[(2r)-2Methylbutanoyl]Oxy}1.2.6.7.8.8a-Hexahydronaphthalen-1-Yl)-3.5Dihydroxyheptanoic Acid

experimental

422.27

3.754

3

6

11

104.06

0.565

Act

P

DB04324

Ovalicin

experimental

298.18

0.905

2

5

4

79.29

0.563

Act

P

DB04376

13-Acetylphorbol

experimental

406.20

-0.741

4

7

3

124.29

0.566

Act

P

DB04392

Bishydroxy[2h-1Benzopyran-2-One.1.2Benzopyrone]

experimental

336.06

0.156

0

4

2

86.74

0.573

Act

P

DB04599

Aniracetam

experimental

219.09

0.369

0

3

3

46.61

0.617

Act

P

DB04641

3.7-Dihydroxynaphthalene2-carboxylic acid

experimental

204.04

0.914

3

2

1

77.76

0.659

Act

P

experimental

362.13

2.409

2

1

3

58.92

0.583

Act

P

experimental

293.18

4.138

0

2

4

20.31

0.556

Act

P

(5e.13e)-11-hydroxy-9.15DB07177 dioxoprosta-5.13-dien-1-oic acid

experimental

350.21

2.517

2

5

12

91.67

0.598

Act

P

(2E)-1-[2-hydroxy-4-methoxy-5-(3-methylbut-2-en-1DB07500 yl)phenyl]-3-(4-hydroxyphenyl)prop-2-en-1-one

experimental

338.15

3.613

2

1

6

66.76

0.573

Act

P

(2r.3r.4s)-3-(4hydroxyphenyl)-4-methyl-2DB07567 [4-(2-pyrrolidin-1-ylethoxy) phenyl]chroman-6-ol

experimental

445.23

2.847

2

1

6

62.16

0.58

Act

P

(3r.4s.5s.7r.9e.11r.12r)-12ethyl-4-hydroxy-3.5.7.11DB07703 tetramethyloxacyclododec-9ene-2.8-dione

experimental

296.20

2.136

1

4

1

63.6

0.6

Act

P

N-(1-benzylpiperidin-4-yl)4-sulfanylbutanamide

experimental

292.16

2.058

1

3

7

71.14

0.708

Act

P

N-[1-(2.6DB07735 dimethoxybenzyl)piperidin4-yl]-4-sulfanylbutanamide

experimental

352.18

1.905

1

3

9

89.6

0.552

Act

P

DB07123

DB07734

n-(4-methylbenzoyl)-4benzylpiperidine

PSAf

Estimated potencyi

Compound

(3aS.4R.9bR)-2.2-difluoro-4(4-hydroxyphenyl)-6DB07036 (methoxymethyl)1.2.3.3a.4.9b-hexahydrocyclopenta[c]chromen-8-ol



ID*



1483

(Table 4) contd….


PSAf


Estimated potencyi

ID*

Compound

Drug Group

MWa

LogPb

DB07883

(2-amino-3-phenylbicyclo[2.2.1]hept-2-yl)phenyl-methanone

experimental

291.16

2.801

1

2

3

43.09

0.602

Act

P

DB07933

(3as.4r.9br)-4-(4-hydroxyphenyl)-1.2.3.3a.4.9bhexahydrocyclopenta[c] chromen-8-ol

experimental

282.13

2.645

2

0

1

49.69

0.598

Act

P

DB08020

(3as.4r.9br)-4-(4-hydroxyphenyl)-6-(methoxymethyl)1.2.3.3a.4.9b-hexahydrocyclopenta[c]chromen-8-ol

experimental

326.15

2.308

2

1

3

58.92

0.631

Act

P

DB08224

(1s.7s.8s.8ar)-1.2.3.7.8.8ahexahydro-7-methyl-8-[2[(2r.4r)-tetrahydro-4-hy droxy-6-oxo-2h-pyran-2yl]ethyl]-1-naphthalenol

experimental

306.18

2.215

2

4

3

66.76

0.558

Act

P

DB08517

(2S)-5-hydroxy-2-(4hydroxyphenyl)-7-methoxy2.3-dihydro-4H-chromen-4one

experimental

286.08

1.111

2

1

2

75.99

0.806

Act

P

DB08737

(3as.4r.9br)-4-(4-hydroxyphenyl)-1.2.3.3a.4.9bhexahydrocyclopenta[c]chromen-9-ol

experimental

282.16

2.645

2

0

1

49.69

0.651

Act

P

* ID =Code of DrugBank;aMW = Molecular weight; bLogP = Computed octanol/water partition coefficient; cnHBDon = Number of hydrogen bond donors; dnHBAc = Number of hydrogen bond acceptors; fPSA = Polar surface area; gSf =Fused Score for the maximum of the similarity; hAct =Active against the tyrosinase identified by Clasiffication MCS QSAR; i P = Potent inhibitor of tyrosinase identified by Potency MCS QSAR.

CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest.

[5]

ACKNOWLEDGEMENTS

[6]

Authors acknowledge the supports from a National Foundation for Science and Technology of Vietnam (NAFOSTED, Grant number 106-YS.05-2014.01). CasañolaMartin. G.M. thanks the program ‘Estades Temporals per a Investigadors Convidats’ for a fellowship to research at Valencia University (2013-2014). Marrero-Ponce, Y. thanks to the program ‘International Professor’ for a fellowship to work at Cartagena University in 2013-2014.

[7] [8]

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Received: December 10, 2013

Revised: February 10, 2014

Accepted: February 11, 2014

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